Introduction
Bias is closely linked with the process of drawing conclusions based on an intuitive approach rather than analytic methods. Some sources state that bias may be represented by perception errors that occur due to human thinking limitations or irrelevant thinking models (Hammond et al., 2021). Naturally, bias is a subconscious instrument that is meant to utilize collected experience in order to avoid danger and prevent harm. Nonetheless, people learn not only from personal experience but also from controversial information that might be received from culture, the media, and other people.
Moreover, people may tend to form attitudes towards general based on specific, which may also cause bias. According to some sources, bias may be divided into two categories, including implicit bias and explicit bias (Hammond et al., 2021). By definition, implicit bias is a set of unconscious beliefs and perceptions. As an individual may not be aware of implicit personal biases, it may be particularly hard to reduce them. By contrast, explicit biases occur on a conscious level, and hence fewer efforts may be required to identify and reduce them.
Influence of Biases on Executive Performance
As already mentioned, bias may serve as a helpful tool that systemizes and implements knowledge and experience regardless of the field of expertise. However, errors may occur due to inadequate analysis of past experiences or even as a result of false memories. Such errors may form an inappropriate bias, which may have an adverse impact on professional performance. As my selected specialty track is executive nursing, bias may significantly influence outcomes.
Recent studies have shown that bias may form diverse behavior models and unequal treatment based on such factors as race, gender, age, or ethnicity (White et al., 2017). In addition, executive nursing involves management and particularly human resource management. Therefore, implicit bias in executive nursing may cause insufficient attention to professionalism, experience, and competence of employees. It may also lead to inadequate treatment of patients, worsening outcomes, and even causing discrimination.
Personal Biases
In most cases, my personal biases are implicit and based on common stereotypes regarding certain social groups. Even though I try to minimize the adverse effects of unfounded bias, it may not always be possible. For instance, I have an explicit bias towards older people. I also may not always treat with sufficient respect traditions and beliefs, which are unfamiliar and hence hard to understand for me. Another possible bias may be represented by frequently jumping to conclusions based on my perception of weight and the overall fitness of the patient regardless of the background.
Implicit Bias
In some cases, I may assume that older people are not familiar with modern technology, and hence I tend to propose help. However, the benefits of such an explicit bias are closely linked with the drawbars of consequent implicit biases. I may unconsciously believe that older people are not able to use digital technology and modern data sources properly. Such an assumption may lead to unequal treatment of employees and even patients depending on their age groups. Furthermore, as digital technology becomes an inseparable part of modern medicine, such bias towards older people may lead to incorrect assessment of their professional skills.
Bias Reduction Strategies
It may be possible to reduce the above-mentioned bias by stereotype replacement. However, such an approach would also mitigate the benefits of explicit bias. Therefore, individuation, which implies an individual approach to each patient or worker, may be more effective. Biases may occur on different levels and from various sources, making it almost impossible to be fully self-aware. As biases may influence the workflow, form working environments, and affect patient outcomes, it may be critical to address the issue. Regularly assessing experiences and evaluating and analyzing both explicit and implicit biases may be the key to identifying possible adverse effects. Finally, implementing appropriate strategies designed to reduce bias is essential.
References
Hammond, M. E., Stehlik, J., Drakos, S. G., & Kfoury, A. G. (2021). Bias in medicine. JACC: Basic to Translational Science, 6(1), 78–85. Web.
White, A. A., Logghe, H. J., Goodenough, D. A., Barnes, L. L., Hallward, A., Allen, I. M., Green, D. W., Krupat, E., & Llerena-Quinn, R. (2017). Self-Awareness and cultural identity as an effort to reduce bias in medicine. Journal of Racial and Ethnic Health Disparities, 5(1), 34–49. Web.